Systematic mapping of genetic vulnerabilities in head and neck cancer

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Project Summary/Abstract A vast number of mutations contribute to cancer, but the observed non-random combinations of these leading to transformation highlight the importance of hallmark pathways and networks in cancer progression. While many pathways have been implicated in cancer, tumor heterogeneity stemming from different mutagens, tissue of origin, degree of progression, etc. leads to each case exhibiting a unique subset of altered pathways. Taken together, this diversity among cancer types and their origins, compounded by the huge genetic and epigenetic heterogeneity of tumors has complicated the development of targeted cancer treatments. The premise of this project is that cancer mutations converge into genetic interaction networks, and these networks bring together mutations of all varieties, including genes with low frequency of oncogenic mutations, and tumor suppressor profiles. We thus hypothesize that by systematically mapping these networks in both cell lines and in clinically relevant patient-derived xenograft (PDX) models, new molecular targets for cancer therapy can be identified. Focusing on head and neck squamous cell carcinomas (HNSCC), a disease that results in over 250,000 deaths each year worldwide, the aims correspondingly focus on using state-of-the-art high-throughput epistasis mapping via CRISPR-Cas and coupled single-cell analyses to enable systematic interrogation of the functions of individual genes and gene-pairs while notably also assaying the impact of tumor heterogeneity. Towards this we aim to utilize an extensive panel of >50 HNSCC cell lines and >50 PDX models. To enable screening across this vast collection we will utilize focused libraries and cell barcoding strategies that will allow us to efficiently screen across genes and cell panels via integrated pooled experiments. Coupled with rigorous in vitro and in vivo drug and targeted genetic validations in preclinical models, we anticipate our integrated experimental and computational framework will result in unprecedented insights into the underlying tumor biology as well as unraveling of clinically actionable genetic vulnerabilities to advance the practice of precision oncology.